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Dynamic input/output linearization using recurrent neural networks

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Delgado, A., Kambhampati, C. and Warwick, K. (1996) Dynamic input/output linearization using recurrent neural networks. In: IEEE International Conference on Neural Networks 1996, 3-6 Jun 1996, Washington DC, USA, pp. 1721-1726.

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Official URL: http://dx.doi.org/10.1109/ICNN.1996.549160

Abstract/Summary

A dynamic recurrent neural network (DRNN) is used to input/output linearize a control affine system in the globally linearizing control (GLC) structure. The network is trained as a part of a closed loop that involves a PI controller, the goal is to use the network, as a dynamic feedback, to cancel the nonlinear terms of the plant. The stability of the configuration is guarantee if the network and the plant are asymptotically stable and the linearizing input is bounded.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:21642
Uncontrolled Keywords:PI controller, asymptotic stability, closed loop, control affine system, dynamic feedback, dynamic input/output linearization, dynamic recurrent neural network, globally linearizing control
Additional Information:Proceedings ISBN: 0780332105
Publisher:IEEE
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